store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 1000, 'test_batch_size': 1000, 'validation_set_size': 1000, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 10, 'num_iterations': 100, 'no_cuda': False, 'name': 'bald_20_440307', 'seed': 440307, 'log_interval': 10, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[57067, 51186, 32773, 27572, 25807, 27189, 30986, 9610, 379, 32213, 52192, 24255, 7195, 15826, 45548, 30259, 51244, 43290, 44948, 20410]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6815, 'nll': 2.1378473162651064}, 'chosen_samples': [43735, 1586, 832, 39179, 9246, 34964, 15466, 28059, 32070, 56553], 'chosen_samples_score': ['1.1406391', '1.1434828', '1.1485653', '1.1491659', '1.1554148', '1.1613669', '1.2196894', '1.2498193', '1.2675891', '1.1825795']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7154, 'nll': 1.586852765083313}, 'chosen_samples': [30065, 34730, 12682, 42839, 55556, 37497, 3689, 49895, 51570, 53734], 'chosen_samples_score': ['1.0775604', '1.0794886', '1.0795515', '1.0894516', '1.1051902', '1.1020029', '1.1452823', '1.0954378', '1.0974305', '1.0911272']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.71, 'nll': 1.607893419265747}, 'chosen_samples': [27930, 27738, 21447, 53358, 26081, 17621, 42681, 6057, 32299, 42677], 'chosen_samples_score': ['1.0341731', '1.0377817', '1.0488787', '1.0385207', '1.0729568', '1.0917983', '1.2110162', '1.0987077', '1.0758381', '1.104624']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7547, 'nll': 1.34086332321167}, 'chosen_samples': [17817, 44159, 48752, 36760, 31850, 24060, 16975, 20382, 47069, 18489], 'chosen_samples_score': ['0.9413619', '1.0715808', '0.96348166', '0.96103305', '0.95010537', '0.97958153', '0.94730556', '1.0736847', '0.96692795', '0.9490885']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7674, 'nll': 1.2134122014045716}, 'chosen_samples': [37309, 8586, 47614, 14644, 37946, 6474, 31482, 54640, 17237, 50576], 'chosen_samples_score': ['0.93650526', '0.9386954', '0.94413763', '0.94452095', '0.953028', '0.9543131', '0.9608098', '0.9623993', '0.98136646', '0.97629994']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7438, 'nll': 1.2817911028862}, 'chosen_samples': [19829, 47741, 3791, 8691, 55526, 49893, 43946, 51858, 8353, 51976], 'chosen_samples_score': ['0.87428886', '0.87596655', '0.8829613', '0.88578486', '0.8982324', '0.9143058', '0.90257967', '0.8900172', '0.9172191', '1.0172778']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7321, 'nll': 1.1691734075546265}, 'chosen_samples': [36489, 8833, 16909, 340, 7867, 39356, 187, 54403, 13259, 14335], 'chosen_samples_score': ['0.83564425', '0.8425285', '0.8441017', '0.8517384', '0.85579914', '0.85983294', '0.8611348', '0.8628909', '0.90920293', '0.9223552']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7971, 'nll': 0.9471977829933167}, 'chosen_samples': [12305, 32126, 36471, 21527, 24982, 11417, 54191, 55513, 54711, 11500], 'chosen_samples_score': ['0.73379457', '0.7517503', '0.74124956', '0.7455855', '0.82408696', '0.7493213', '0.7572243', '0.79087716', '0.75771725', '0.75949556']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8372, 'nll': 0.862943172454834}, 'chosen_samples': [47260, 16600, 59289, 49809, 20059, 16882, 14679, 9433, 6130, 22561], 'chosen_samples_score': ['0.9832051', '0.99511415', '0.99198216', '0.9996513', '1.003007', '1.0519817', '1.0846477', '1.0185738', '1.0086942', '1.0287354']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8514, 'nll': 0.8941580951213837}, 'chosen_samples': [40332, 59314, 56878, 18246, 16580, 29704, 46368, 59427, 59783, 12345], 'chosen_samples_score': ['1.0367827', '1.0477228', '1.0421284', '1.0485818', '1.1472993', '1.084804', '1.0779893', '1.0934734', '1.0652874', '1.0595665']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.873, 'nll': 0.7079257220029831}, 'chosen_samples': [11693, 51464, 37414, 53223, 34304, 25332, 42951, 13842, 29145, 35401], 'chosen_samples_score': ['0.9063366', '0.9072105', '0.91066974', '0.92208326', '0.92733526', '0.9299456', '0.9320272', '1.0078111', '0.9380345', '0.9463452']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8825, 'nll': 0.6518551409244537}, 'chosen_samples': [45488, 24513, 27837, 39453, 18317, 52358, 23021, 41933, 48460, 33017], 'chosen_samples_score': ['0.8940527', '0.89602643', '0.90782', '0.9089695', '0.9185026', '0.92041874', '0.9414899', '1.0005785', '0.94447464', '0.9807058']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8957, 'nll': 0.6099322736263275}, 'chosen_samples': [37465, 22169, 17712, 8958, 23712, 13291, 55864, 28512, 56615, 51842], 'chosen_samples_score': ['1.0287802', '1.0299122', '1.0378475', '1.0433159', '1.0609307', '1.0650043', '1.0924118', '1.0617113', '1.0682371', '1.0714483']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9002, 'nll': 0.5778098970651626}, 'chosen_samples': [25803, 14749, 43856, 27395, 52624, 45728, 36744, 32693, 9608, 34328], 'chosen_samples_score': ['0.8662507', '0.86638504', '0.903135', '0.86750704', '0.8895353', '0.8941894', '0.9100623', '0.8923463', '0.8684789', '0.8867396']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9008, 'nll': 0.5971414297819138}, 'chosen_samples': [50078, 21503, 5689, 6944, 31650, 14715, 38760, 51309, 47473, 59361], 'chosen_samples_score': ['0.9712754', '0.97580194', '1.0074141', '0.9765908', '1.0095508', '1.1045904', '1.0561464', '1.0299735', '1.0410076', '1.0196795']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9113, 'nll': 0.528320574760437}, 'chosen_samples': [7984, 30884, 11208, 57728, 40589, 32276, 14687, 3730, 50930, 16888], 'chosen_samples_score': ['0.87039953', '0.89670396', '0.90335757', '0.9090633', '0.98349863', '0.95624155', '0.9575073', '0.92397815', '0.96588254', '0.9091694']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9139, 'nll': 0.584651631116867}, 'chosen_samples': [29335, 19888, 40654, 31301, 3931, 20110, 22591, 29899, 24479, 11482], 'chosen_samples_score': ['0.97807115', '0.98931783', '0.9930586', '0.9968597', '0.99606097', '1.0038465', '1.0656731', '1.0359002', '1.0468705', '1.050869']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9184, 'nll': 0.5383667647838593}, 'chosen_samples': [17417, 13318, 37984, 25546, 33812, 30853, 55496, 28954, 19855, 42687], 'chosen_samples_score': ['0.9166036', '0.9226005', '0.94260216', '0.92912835', '0.94564664', '0.9512138', '1.0039477', '0.98691654', '0.9562326', '0.99607235']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9356, 'nll': 0.45996530055999757}, 'chosen_samples': [4797, 38085, 32519, 56191, 12036, 3810, 47870, 56303, 2845, 34524], 'chosen_samples_score': ['0.98052794', '0.9916356', '0.9923809', '1.0053011', '1.0153747', '1.0553043', '1.0462658', '1.0183716', '1.0278361', '1.0679991']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9418, 'nll': 0.45081539154052735}, 'chosen_samples': [21700, 32045, 36818, 49493, 32880, 19396, 25783, 24620, 43212, 40678], 'chosen_samples_score': ['1.0193169', '1.0239849', '1.0363078', '1.0330791', '1.0364475', '1.0411545', '1.0893061', '1.0936303', '1.0431944', '1.1095337']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.933, 'nll': 0.46143320500850676}, 'chosen_samples': [58560, 55638, 34765, 24385, 59343, 25994, 8771, 28508, 3719, 5842], 'chosen_samples_score': ['0.9157148', '0.9222787', '0.9207587', '0.9250018', '0.959481', '0.99973476', '1.0153742', '0.93165326', '1.0334375', '0.94401985']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9338, 'nll': 0.4616371154785156}, 'chosen_samples': [31664, 33062, 41295, 10210, 33594, 33338, 33505, 43126, 57474, 37347], 'chosen_samples_score': ['0.82042676', '0.8278717', '0.8298665', '0.83284986', '0.86594164', '0.84759337', '0.85285044', '0.9233885', '1.0094824', '0.8430708']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9501, 'nll': 0.40657167583703996}, 'chosen_samples': [12066, 635, 28632, 53873, 18322, 37778, 44661, 38275, 30011, 20169], 'chosen_samples_score': ['1.0412359', '1.0829604', '1.1477876', '1.1351494', '1.0580461', '1.0970932', '1.1075709', '1.0522094', '1.1202421', '1.053168']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9405, 'nll': 0.42341983914375303}, 'chosen_samples': [39516, 52169, 15494, 46412, 5175, 20190, 1573, 20869, 10997, 43176], 'chosen_samples_score': ['0.9059898', '0.90663016', '0.9271867', '0.92527443', '0.96031', '0.9865861', '0.9676345', '1.0464375', '0.9615752', '0.98748']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9378, 'nll': 0.44920678734779357}, 'chosen_samples': [3056, 37672, 55683, 52294, 19318, 22272, 18031, 41713, 12986, 56199], 'chosen_samples_score': ['0.7841444', '0.7856627', '0.7860697', '0.8063122', '0.8069092', '0.85439014', '0.82095087', '0.8322493', '0.81199753', '0.8109533']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9534, 'nll': 0.3953606352210045}, 'chosen_samples': [57972, 29530, 16628, 21023, 41371, 54937, 30159, 52761, 28801, 26358], 'chosen_samples_score': ['0.98757213', '0.9900882', '0.9915529', '1.0120301', '1.0659032', '1.0135932', '1.0202742', '0.9923782', '1.0369358', '0.9972915']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9565, 'nll': 0.3347857490181923}, 'chosen_samples': [38080, 36450, 55206, 33162, 5679, 11960, 22083, 37078, 25879, 20172], 'chosen_samples_score': ['0.95816916', '0.96628964', '0.9689836', '0.9709196', '1.0683032', '1.0335886', '0.97222644', '1.0342851', '0.9987581', '1.0381174']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9567, 'nll': 0.36855592429637907}, 'chosen_samples': [8645, 57956, 44870, 5600, 30692, 5013, 20230, 3470, 10992, 13376], 'chosen_samples_score': ['0.8680565', '0.8728823', '0.88359904', '0.9033438', '0.96292293', '0.9600129', '0.90704155', '0.90054417', '0.8988538', '0.8858624']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9554, 'nll': 0.3763531506061554}, 'chosen_samples': [37049, 31293, 52462, 9948, 34115, 55878, 30451, 31184, 14935, 14852], 'chosen_samples_score': ['0.8189413', '0.82448673', '0.8336195', '0.8433189', '0.84395546', '0.97298235', '0.91882026', '0.89588714', '0.86717075', '0.8552185']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9608, 'nll': 0.3573520079255104}, 'chosen_samples': [32735, 52086, 18150, 36072, 4185, 30658, 18473, 32747, 10256, 17540], 'chosen_samples_score': ['1.0102606', '1.0154042', '1.0205013', '1.043843', '1.0486485', '1.0415977', '1.046672', '1.0658221', '1.0272726', '1.092502']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9618, 'nll': 0.3329182341694832}, 'chosen_samples': [41218, 10746, 48102, 38369, 49215, 7478, 10894, 19546, 13998, 34406], 'chosen_samples_score': ['0.9591614', '0.9972149', '0.96501124', '0.97769195', '0.9885004', '0.9894159', '0.97811395', '0.9753567', '0.95992166', '1.0379403']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9673, 'nll': 0.3124256908893585}, 'chosen_samples': [6905, 53872, 36268, 14896, 29672, 3367, 46247, 826, 36141, 54950], 'chosen_samples_score': ['0.9909136', '1.007611', '1.0017076', '1.0084636', '1.0176105', '1.0438766', '1.0298381', '1.0490534', '1.0536305', '1.0872536']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9646, 'nll': 0.317102712392807}, 'chosen_samples': [18008, 23086, 49525, 42078, 49958, 42945, 7259, 43796, 56457, 49890], 'chosen_samples_score': ['0.84049016', '0.86013883', '0.86056936', '0.9339839', '0.8737904', '0.8796784', '0.9042282', '0.8794886', '0.8947986', '0.8710802']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9684, 'nll': 0.31711133420467374}, 'chosen_samples': [53638, 35688, 43874, 4153, 1512, 35882, 24462, 19942, 18598, 13374], 'chosen_samples_score': ['0.8937421', '0.94243604', '0.9208559', '0.90619427', '0.8951936', '0.9357619', '0.94216853', '0.9208913', '0.9096219', '0.9184327']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9675, 'nll': 0.3175965026021004}, 'chosen_samples': [784, 6291, 12651, 45602, 788, 19613, 32776, 14201, 4822, 14405], 'chosen_samples_score': ['0.9515014', '0.9597743', '0.96776944', '0.98641914', '0.9872223', '0.9890575', '1.0248432', '1.0236924', '1.0163617', '1.0611438']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9672, 'nll': 0.3084276124835014}, 'chosen_samples': [48038, 6920, 966, 13428, 38165, 51856, 14894, 470, 36548, 35916], 'chosen_samples_score': ['0.96259725', '0.9698559', '0.9910513', '0.9987414', '1.0182393', '1.1135261', '1.0243213', '1.0337346', '1.0260727', '1.107178']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9715, 'nll': 0.2894320532679558}, 'chosen_samples': [59747, 13709, 40298, 12268, 32994, 45954, 56014, 424, 55739, 13969], 'chosen_samples_score': ['0.9873164', '0.9897729', '1.001966', '1.0026166', '1.026236', '1.0664117', '1.0530595', '1.0673532', '1.2145305', '1.0730922']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9684, 'nll': 0.2968270808458328}, 'chosen_samples': [27176, 50317, 30493, 46524, 31197, 29711, 3392, 15106, 517, 16795], 'chosen_samples_score': ['0.97614557', '1.0204755', '1.0018016', '1.046413', '1.0536517', '1.0567446', '1.0864824', '1.1120355', '1.1138873', '1.0858126']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9687, 'nll': 0.2933656647801399}, 'chosen_samples': [49082, 15134, 23730, 1075, 51993, 33426, 50714, 10756, 39526, 22563], 'chosen_samples_score': ['0.8789884', '0.91936755', '0.9759734', '0.8946004', '0.9851457', '0.92920536', '0.9589713', '0.88157934', '0.9041912', '0.91332173']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.972, 'nll': 0.29519518464803696}, 'chosen_samples': [27358, 42020, 49463, 30604, 11378, 45761, 718, 9552, 28357, 4634], 'chosen_samples_score': ['0.9997191', '1.0107642', '1.0164082', '1.0434729', '1.029363', '1.0403179', '1.0466187', '1.0599887', '1.0984728', '1.1595464']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.974, 'nll': 0.2803469389677048}, 'chosen_samples': [36770, 45422, 51988, 50514, 32499, 56662, 49474, 10332, 29713, 47845], 'chosen_samples_score': ['1.0488037', '1.0530281', '1.0551901', '1.0655159', '1.1570885', '1.0772898', '1.0810397', '1.1386979', '1.0851799', '1.0929501']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9722, 'nll': 0.2772008150815964}, 'chosen_samples': [34771, 44570, 19866, 20859, 30521, 14385, 35864, 49563, 9774, 10273], 'chosen_samples_score': ['0.9982766', '1.1033441', '1.01128', '1.0329676', '1.0346229', '1.0433397', '1.0549977', '1.0039443', '1.0270488', '1.0083199']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9739, 'nll': 0.27426121234893797}, 'chosen_samples': [41578, 39355, 41426, 34847, 5163, 52661, 2381, 24587, 9448, 19495], 'chosen_samples_score': ['0.8658681', '0.8956915', '0.9040826', '0.9818141', '0.9077098', '0.88171154', '0.88432854', '0.9565187', '0.8803385', '0.9064393']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9758, 'nll': 0.2634544983506203}, 'chosen_samples': [42673, 26577, 46122, 48454, 25246, 34378, 6846, 11572, 11292, 12426], 'chosen_samples_score': ['1.0252049', '1.0337563', '1.0367593', '1.0380119', '1.1365217', '1.0732293', '1.2987405', '1.0530581', '1.0694544', '1.1122687']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9715, 'nll': 0.2905251353979111}, 'chosen_samples': [32814, 55906, 47479, 40046, 20709, 28368, 42973, 3676, 49002, 42703], 'chosen_samples_score': ['0.95976424', '0.96098924', '0.9637756', '0.9639534', '0.9657534', '1.0181954', '1.0404093', '1.0497234', '0.96800697', '1.0604439']})
store['iterations'].append({'num_epochs': 17, 'test_metrics': {'accuracy': 0.9729, 'nll': 0.2886573448777199}, 'chosen_samples': [53910, 8690, 59286, 3030, 4646, 45502, 17603, 43950, 21361, 38577], 'chosen_samples_score': ['1.0044191', '1.0054562', '1.0068476', '1.024586', '1.0384015', '1.0494224', '1.1016841', '1.104327', '1.0558178', '1.1036261']})
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store['iterations'].append({'num_epochs': 22, 'test_metrics': {'accuracy': 0.9841, 'nll': 0.18668687716126442}, 'chosen_samples': [10195, 50403, 8228, 57575, 6220, 51618, 7833, 9687, 49515, 16676], 'chosen_samples_score': ['0.8642527', '0.8662084', '0.8668584', '0.8740972', '0.9900457', '1.0529618', '0.8802902', '0.93127215', '0.8740275', '0.8711682']})
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store['iterations'].append({'num_epochs': 24, 'test_metrics': {'accuracy': 0.9837, 'nll': 0.17738983780145645}, 'chosen_samples': [7308, 12514, 17663, 2426, 50946, 5052, 52610, 34839, 51764, 48966], 'chosen_samples_score': ['0.851963', '0.8521433', '0.8637911', '0.8594615', '0.86418325', '0.9449143', '0.89744574', '0.8867407', '0.9899654', '0.909981']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9832, 'nll': 0.19422616213560104}, 'chosen_samples': [32250, 18504, 5302, 37974, 37838, 19868, 16022, 12377, 30574, 21436], 'chosen_samples_score': ['0.7563321', '0.75881207', '0.76696813', '0.7732586', '0.7836762', '0.773336', '0.7925352', '0.91324776', '0.83493996', '0.83120316']})
